Faculty Profile

Mark Albert

Title
Assistant Professor
Department
Computer Science and Engineering
College
College of Engineering

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, Cornell University, 2010. 
Major: Computational Biology
Dissertation Title: Normative Visual Development: innate learning in the early visual system
Carnegie Mellon University, 2004. 
Major: Computational Neuroscience
University of Pittsburgh-Pittsburgh Campus, 2004. 
Major: Computational Neuroscience
Universität Wien, 2001. 
Major: AI-Related Coursework
BS, Pittsburg State University, 2000. 
Major: Computer Science, Chemistry, Physics, and Mathematics





          

        
                

          
Current Scheduled Teaching*


          
CSCE 5934.712, Directed Study, Spring 2021 
CSCE 6950.712, Doctoral Dissertation, Spring 2021 
INFO 6950.701, Doctoral Dissertation, Spring 2021 
CSCE 6940.712, Individual Research, Spring 2021 
INFO 6900.707, Special Problems, Spring 2021 
BMEN 5900.809, Special Problems in Biomedical Engineering, Spring 2021 


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  


    

    

      


        


          


            
Previous Scheduled Teaching*


            
                    

                    BMEN 5280.001, AI for Wearables and Healthcare, Fall 2020 
                    

                    CSCE 5280.001, AI for Wearables and Healthcare, Fall 2020 
                    

                    INFO 6950.709, Doctoral Dissertation, Fall 2020 
                    

                    CSCE 6940.712, Individual Research, Fall 2020 
                    

                    CSCE 5950.712, Master's Thesis, Fall 2020 
                    

                    CSCE 5214.001, Software Development for Artificial Intelligence, Fall 2020 
                    

                    CSCE 5214.004, Software Development for Artificial Intelligence, Fall 2020 
                    

                    CSCE 5214.008, Software Development for Artificial Intelligence, Fall 2020 
                    

                    CSCE 2900.712, Special Problems in Computer Science and Engineering, Fall 2020 
                    

                    CSCE 2999Z.712, CSCE Research, Summer 10W 2020 
                    

                    CSCE 5934.712, Directed Study, Spring 2020 
                    

                    CSCE 6940.712, Individual Research, Spring 2020 
                    

                    INFO 6660.012, Readings in Information Science, Spring 2020 
                    

                    CSCE 4930.712, Topics in Computer Science and Engineering, Spring 2020 Syllabus
                    

                    CSCE 5933.712, Topics in Computer Science and Engineering, Spring 2020 Syllabus
                    

                    CSCE 4890.712, Directed Study, Fall 2019 
                    

                    CSCE 5934.712, Directed Study, Fall 2019 
                    

                    CSCE 3996.002, Honors College Mentored Research Experience, Fall 2019 
                    

                    CSCE 6940.712, Individual Research, Fall 8W2 2019 
                    

                    CSCE 6940.712, Individual Research, Fall 2019 
                    

                    CSCE 4205.001, Introduction to Machine Learning, Fall 2019 Syllabus SPOT
                    

                    CSCE 5215.001, Machine Learning, Fall 2019  SPOT
                    

                    CSCE 5215.600, Machine Learning, Fall 2019  SPOT

          


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  

  

  	

		

		,  
			
				

					
					Overall
Summative Rating		
					Challenge and
Engagement Index		
					Response Rate
					
				
  			
			
			
				

					
					
out of 5		
					
out of 7		
					%
 of 
students responded		
					
					
				

			
		


		
    
			
  • Overall Summative Rating (median):
    
			This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
				
      
					
    • The course as a whole was
      • 
					
    • The course content was
      • 
					
    • The instructor’s contribution to the course was
      • 
					
    • The instructor’s effectiveness in teaching the subject matter was
      • 
				
    
			
    • 
			
  • Challenge and Engagement Index:
    
			This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
				
      
					
    • Do you expect your grade in this course to be
      • 
					
    • The intellectual challenge presented was
      • 
					
    • The amount of effort you put into this course was
      • 
					
    • The amount of effort to succeed in this course was
      • 
					
    • Your involvement in course (doing assignments, attending classes, etc.) was
      • 
				
    
			
    • 
		

		CLOSE
  	

  






  
        




      
Top